A COMPARISION OF DIFFERENT MULTI- INTERVAL DISCRETIZATION METHODS FOR DECISION TREE LEARNING
Sascha Trautzsch, Petra Perner · 2007
. Properly addressing the discretization process of continous valued features is an important problem during decision tree learning. This paper describes four multi-interval discretization methods for induction of decision trees used in dynamic fashion. We compare two known discretization methods to two new methods proposed in this paper based on a histogram based method and a neural net based method (LVQ). We compare them according to accuracy of the resulting decision tree and to compactness of the tree. For our comparison we use three data bases, IRIS domain, satellite domain and OHS domain (ovariel hyper stimulation). Key Words. Machine Learning, Decision Tree, Multi-Interval Discretization 1 INTRODUCTION Decision tree learning is a widely used method for pattern recognition and image interpretation [LiF83][HMM94][PBY96]. Properly addressing the discretization process of continuous-valued features is an important problem during decision tree learning. Decision tree learn...